My experience

LLM

Using generative AI and LLM to analyze and understand textual data and generate sql queries and tabular data from data model/database.

Web Scrapping

Extracting and collecting data from websites using python library like Beautiful Soup and Scrapy for pulling data out of HTML.Used these library for pulling data from a E-commerce site for analysing sentiment or polarity for a certain product.

Sentiment analysis

Analysing sentiment from a given set of textual data.Either it can be any comment,review or tweet.For sentiment analysis I have used many deep learning models like CNN , RNN(LSTM,GRU) and XGBOOST.I have experinece in using libraries like keras(Tensorflow backend), Genism, Sci-kit learn, NLTK and many more.Also have a Clear understanding of word embedding.

Here Are some of the projects::::
Play Store Review Analysis
Hate Speech Detection

Data Clustering

Grouping comparatively more similar entities together using clusterung algorithms like connectivity models,distribution models and centroid models.K-means clustering,DBSCAN are some of the techniques I have experience working on.

Topic Modeling

Understanding and extracting hidden and abstract topics from large volumes of text.The latent semantics of the documnets are extracted discovering hidden topical patterns.LDA, LDA2VEC and Doc2Vec are some of the algorithms that I have worked on

Here Are some of the projects::::
Topic modeling